n2 = '100000'
strong_ext = '_chi2_strong_' + vectorizer
weak_ext = '_chi2_weak_' + vectorizer
train_features, train_labels = load_data.load_feature_data(0, test_train='train')
len_train= len(train_labels)
test_features, test_labels = load_data.load_feature_data(0, test_train='test')
all_labels = np.append(train_labels, test_labels)
## Do better Normalization on Customer Features
all_features = np.vstack((train_features, test_features))
float_feats = [[float(i) for i in row] for row in all_features] # Turn values to floating point
new_feats = preprocessing.normalize(float_feats, norm='l1', axis=0)
train_features = new_feats[:len_train]
test_features = new_feats[len_train:]
### Done Normalizing
train_email_features, test_email_features = load_data.load_email_data(0, vectorizer, stemmer=stem, vectorizer=v2)
train_subject_features, test_subject_features = load_data.load_subject_data(0, vectorizer, stemmer=stem, vectorizer=v2)
train_email_features_n1, test_email_features_n1 = load_data.load_email_data(0, n1+v2, stemmer=stem, vectorizer=v2)
train_subject_features_n1, test_subject_features_n1 = load_data.load_subject_data(0, n1+v2, stemmer=stem, vectorizer=v2)
train_email_features_n2, test_email_features_n2 = load_data.load_email_data(0, n2+v2, stemmer=stem, vectorizer=v2)
train_subject_features_n2, test_subject_features_n2 = load_data.load_subject_data(0, n2+v2, stemmer=stem, vectorizer=v2)
train_email_strong_features, test_email_strong_features = load_data.load_email_data(0, strong_ext, stemmer=stem, vectorizer=vectorizer)
train_subject_strong_features, test_subject_strong_features = load_data.load_subject_data(0, strong_ext, stemmer=stem, vectorizer=vectorizer)
train_email_weak_features, test_email_weak_features = load_data.load_email_data(0, weak_ext, stemmer=stem, vectorizer=vectorizer)
train_subject_weak_features, test_subject_weak_features = load_data.load_subject_data(0, weak_ext, stemmer=stem, vectorizer=vectorizer)
logging.info("All Data Loaded")
for t in ['email']:
for s in ['n2']:
if t == 'email':
if s =='strong':
trainer = train_email_strong_features
stemmers = ['RegexpStemmer', 'LancasterStemmer', 'PorterStemmer']
for vectorizer in vectorizers:
for stem in stemmers:
train_features, train_labels = load_data.load_feature_data(0, test_train='train')
len_train= len(train_labels)
test_features, test_labels = load_data.load_feature_data(0, test_train='test')
all_labels = np.append(train_labels, test_labels)
## Do better Normalization on Customer Features
all_features = np.vstack((train_features, test_features))
float_feats = [[float(i) for i in row] for row in all_features] # Turn values to floating point
new_feats = preprocessing.normalize(float_feats, norm='l1', axis=0)
train_features = new_feats[:len_train]
test_features = new_feats[len_train:]
### Done Normalizing
train_email_features, test_email_features = load_data.load_email_data(0, vectorizer, stemmer=stem, vectorizer=vectorizer)
train_subject_features, test_subject_features = load_data.load_subject_data(0, vectorizer, stemmer=stem, vectorizer=vectorizer)
logging.info("++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++")
logging.info("++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++")
logging.info("++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++")
logging.info("VECTORIZER = %s" % vectorizer)
logging.info("STEMMER = %s" % stem)
logging.info("++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++")
logging.info("++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++")
logging.info("++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++")
logging.info("All Data Loaded")
for t in ['both', 'all', 'subject', 'email', 'normal']:
if t == 'email':
trainer = train_email_features
test = test_email_features
